Robust Optimization for Uncertain Radiobiological Parameters in Inverse Dose Planning

Abstract: Cancer is a common cause of death worldwide with radiotherapy as one of the most used treatments. Radiation treatment plans are normally optimized using constraints on the maximum dose to tumours and minimum dose to surrounding healthy structures. It has been suggested that utilizing biological models in the radiation plan optimization process could improve outcome significantly. Such treatment plans depend not only on the accuracy of the biological models, describing the dose response relations of different tumours and other structures, but also on the accuracy of tissue specific parameters in these models. Different sets of biological model parameters lead to different treatment plans and thus, uncertainties in these parameters may compromise the quality of the treatments. In this thesis, several radiobiological optimization models have been developed, including either the concepts of Tumour Control Probability (TCP) and Normal Tissue Complication Probability (NTCP), or Equivalent Uniform Dose (EUD). The uncertainties of model parameters are expressed by probability density functions included in the dose optimization process. Robust optimization methods that account for the uncertainties have been developed and implemented in a MATLAB GUI created for Gamma Knife surgery. The robust optimized dose plans have been compared to non-robust plans using fixed parameter values. The results suggest that the final dose distribution strongly depend on the distribution functions and that the robust treatment plans are less dependent on variations in the model parameters